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author | Daniel Friesel <daniel.friesel@uos.de> | 2020-07-06 12:46:59 +0200 |
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committer | Daniel Friesel <daniel.friesel@uos.de> | 2020-07-06 12:46:59 +0200 |
commit | 21e29a8e9b92d34cfcc241188b5e4b903dd9c4df (patch) | |
tree | acc235361070e67316003431f8276dc255565ccb /bin | |
parent | 9e1d0997e39fe17b92c7971fc6fcd21d7dfb87d2 (diff) |
Move keysightdlog to bin
Diffstat (limited to 'bin')
-rwxr-xr-x | bin/keysightdlog.py | 164 |
1 files changed, 164 insertions, 0 deletions
diff --git a/bin/keysightdlog.py b/bin/keysightdlog.py new file mode 100755 index 0000000..89264b9 --- /dev/null +++ b/bin/keysightdlog.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 + +import lzma +import matplotlib.pyplot as plt +import numpy as np +import os +import struct +import sys +import xml.etree.ElementTree as ET + + +def plot_y(Y, **kwargs): + plot_xy(np.arange(len(Y)), Y, **kwargs) + + +def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None): + fig, ax1 = plt.subplots(figsize=(10, 6)) + if title != None: + fig.canvas.set_window_title(title) + if xlabel != None: + ax1.set_xlabel(xlabel) + if ylabel != None: + ax1.set_ylabel(ylabel) + plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99) + plt.plot(X, Y, "bo", markersize=2) + if output: + plt.savefig(output) + with open("{}.txt".format(output), "w") as f: + print("X Y", file=f) + for i in range(len(X)): + print("{} {}".format(X[i], Y[i]), file=f) + else: + plt.show() + + +filename = sys.argv[1] + +with open(filename, "rb") as logfile: + lines = [] + line = "" + + if ".xz" in filename: + f = lzma.open(logfile) + else: + f = logfile + + while line != "</dlog>\n": + line = f.readline().decode() + lines.append(line) + xml_header = "".join(lines) + raw_header = f.read(8) + data_offset = f.tell() + raw_data = f.read() + + xml_header = xml_header.replace("1ua>", "X1ua>") + xml_header = xml_header.replace("2ua>", "X2ua>") + dlog = ET.fromstring(xml_header) + channels = [] + for channel in dlog.findall("channel"): + channel_id = int(channel.get("id")) + sense_curr = channel.find("sense_curr").text + sense_volt = channel.find("sense_volt").text + model = channel.find("ident").find("model").text + if sense_volt == "1": + channels.append((channel_id, model, "V")) + if sense_curr == "1": + channels.append((channel_id, model, "A")) + + num_channels = len(channels) + duration = int(dlog.find("frame").find("time").text) + interval = float(dlog.find("frame").find("tint").text) + real_duration = interval * int(len(raw_data) / (4 * num_channels)) + + data = np.ndarray( + shape=(num_channels, int(len(raw_data) / (4 * num_channels))), dtype=np.float32 + ) + + iterator = struct.iter_unpack(">f", raw_data) + channel_offset = 0 + measurement_offset = 0 + for value in iterator: + data[channel_offset, measurement_offset] = value[0] + if channel_offset + 1 == num_channels: + channel_offset = 0 + measurement_offset += 1 + else: + channel_offset += 1 + +if int(real_duration) != duration: + print( + "Measurement duration: {:f} of {:d} seconds at {:f} µs per sample".format( + real_duration, duration, interval * 1000000 + ) + ) +else: + print( + "Measurement duration: {:d} seconds at {:f} µs per sample".format( + duration, interval * 1000000 + ) + ) + +for i, channel in enumerate(channels): + channel_id, channel_model, channel_type = channel + print( + "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} {:s}".format( + channel_id, + channel_model, + np.min(data[i]), + np.max(data[i]), + np.mean(data[i]), + channel_type, + ) + ) + + if ( + i > 0 + and channel_type == "A" + and channels[i - 1][2] == "V" + and channel_id == channels[i - 1][0] + ): + power = data[i - 1] * data[i] + power = 3.6 * data[i] + print( + "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} W".format( + channel_id, channel_model, np.min(power), np.max(power), np.mean(power) + ) + ) + min_power = np.min(power) + max_power = np.max(power) + power_border = np.mean([min_power, max_power]) + low_power = power[power < power_border] + high_power = power[power >= power_border] + plot_y(power) + print( + " avg low / high power (delta): {:f} / {:f} ({:f}) W".format( + np.mean(low_power), + np.mean(high_power), + np.mean(high_power) - np.mean(low_power), + ) + ) + # plot_y(low_power) + # plot_y(high_power) + high_power_durations = [] + current_high_power_duration = 0 + for is_hpe in power >= power_border: + if is_hpe: + current_high_power_duration += interval + else: + if current_high_power_duration > 0: + high_power_durations.append(current_high_power_duration) + current_high_power_duration = 0 + print( + " avg high-power duration: {:f} µs".format( + np.mean(high_power_durations) * 1000000 + ) + ) + +# print(xml_header) +# print(raw_header) +# print(channels) +# print(data) +# print(np.mean(data[0])) +# print(np.mean(data[1])) +# print(np.mean(data[0] * data[1])) |